The effective management and efficient use of data is a major challenge for government, academia and industry. The scale, diversity and distributed nature of current and emerging data assets are increasing. For example, every sixty seconds there are 433,000 tweets, 2.66 million Google searches, 4.7 million Tumblr posts, and 139 million emails sent. As data becomes ever more ubiquitous and critical to decision making it is vital that it is turned into meaningful information. The aim of the research discussed in this paper was to determine the usefulness of the EMOTIVE system, developed by the authors, to track the Scottish Independence Referendum and if it could be used to track the general public emotions towards the UK 2015 General Election by creating a new mobile phone application called VOTEBEE (Voter Opinion Tracked by EMOTIVE Engine). In this paper we employ an ontology engineering approach to the problem of fine-grained emotion detection in sparse messages. Messages are also processed using a custom NLP pipeline, which is appropriate for the sparse and informal nature of text encountered on micro-blogs or tweets. Our approach detects a range of eight high-level emotions; anger, confusion, disgust, fear, happiness, sadness, shame and surprise. The paper analyses the Scottish Independence Referendum data using EMOTIVE to determine what can we learn from monitoring social media networks and if there is a requirement to develop systems like VOTEBEE to help determine voter interest and increase debates in the lead up to the 2015 UK General Election.